The gap \(|p-q|\)  reflects structural information and the gap between \(\mathbb {P}_{1},\,\mathbb {P}_{-1}\)  reflects attributed information, e.g., Jensen-Shannon distance \(\text{JS}(\mathbb {P}_{1}, \mathbb {P}_{-1})\)  that is well connected to Bayesian mis-classification error [1]}. Graph learning allows combining these two types of information. In Sec. , we give more specific definitions of these two types of information and their regime for our analysis.
